Interview Bank

How to reason about it · think before answering

1. The keyword is why. Citing the docs is not an answer; explain it in terms of how the model detects content boundaries.
2. Breakdown: the core risk in a long prompt is mixing material, instructions, and examples. Paired tags give each part an explicit start, end, and name, so the model separates them reliably and can reference a specific section in its reply.
3. Versus Markdown: headings and fences delimit but have no explicit closing marker, so pasted material that itself contains Markdown breaks the structure. XML tags are paired, nestable, and freely named, and the benefit grows with messier input.
4. Add the engineering habits: consistent tag names, instructions after the material, and asking the model to cite tags in its answer. Three to six top-level tags is typical.
5. Follow-ups: is there a fixed tag vocabulary? No — structure and semantics matter, consistency within one prompt matters. What if the material contains XML? Pick non-colliding names.

分析过程 · 先想清楚再作答

1. 题眼在「为什么」。只答「官方推荐」等于没答；要能从「模型如何分辨内容边界」这个角度解释。
2. 拆法：长提示词的核心风险是不同性质的内容（材料、指令、示例）混在一起，模型分错边界就会把材料里的句子当指令执行、或把示例当成事实。成对的标签给每一段一个明确的起止和名字，模型分辨边界的准确率更高，也能在回答里精确引用「哪一段」。
3. 与 Markdown 的区别：Markdown 靠标题和围栏分段，但没有显式的结束标记；当贴进去的材料本身含 Markdown（比如一份 README）时容易串位。XML 标签成对、可嵌套、名字自定义，材料越杂优势越大。
4. 补一条工程习惯：标签名前后一致，指令放在材料之后，回答时要求引用标签名。三到六个顶层标签是常态，不要过度包装。
5. 可预期的追问：标签名有没有固定词表？没有，模型看的是结构和语义，但同一个提示词内要一致；另一个追问是「材料本身含 XML 怎么办」——换一个不会撞的标签名，或用 CDATA 式的转义说明。

How to reason about it · think before answering

1. This question shows up in almost every loop. The differentiator is not reciting three definitions, it is offering a decision rule the interviewer can reuse.
2. Lead with the rule: is the model missing knowledge, or missing a way of speaking? Missing knowledge means retrieval; missing style or output shape means fine-tuning. That single cut covers most cases.
3. Then line up the three options against three costs: cost of updating knowledge, cost per request, and whether the answer can be traced back to a source. Retrieval updates by editing a file, fine-tuning takes a retraining cycle, and long-context pays for the whole corpus on every call.
4. Give long-context its fair case: when the corpus is small, changes rarely, and request volume is low, stuffing it in is the cheapest engineering decision you can make. It stops being cheap once the corpus grows or the same material is queried thousands of times a day.
5. Close by naming when none of this applies: if the answer does not depend on any external document (rewriting, translating, reformatting), retrieval only adds noise, latency and cost.
6. Expected follow-up: can you do both? Yes, and it is common. Fine-tuning controls format and refusal behaviour, retrieval supplies the facts.

分析过程 · 先想清楚再作答

1. 这题几乎每场都问，区分度不在能不能背出三条定义，而在你会不会给一条判据。只说「RAG 适合动态知识、微调适合特定风格」的人一抓一大把，面试官等的是下一句。
2. 先给一条能当场套用的判据：模型缺的是「知道什么」还是「怎么说」。缺知识走检索，缺风格与输出格式走微调，这一刀切下去能分掉八成场景。
3. 再拿三笔账把三条路排开：知识更新的代价（改文件立刻生效 / 重训以天计 / 改文件立刻生效）、单次成本（只付取回的几段 / 只付推理 / 每次都付全量材料）、能不能归因（能 / 不能 / 能但材料一多定位会飘）。
4. 把上下文直塞的适用边界说清楚：材料总量小、更新不频繁、对单次成本不敏感的场景它最划算，因为工程量近乎为零。一旦材料涨到几百篇，或者同一批材料每天要被问上万次，成本曲线立刻反超。
5. 最后主动补一句「什么时候都不该用检索」——任务的答案不依赖任何外部文档时（改写、翻译、格式转换），加检索只会引入噪声、延迟和成本。能主动划出不该用的边界，比会背适用场景更能证明你做过。
6. 可预期的追问：能不能既微调又检索？答案是可以，而且常见——微调管输出格式与拒答口径，检索管事实，两者解决的不是同一个问题。

How to reason about it · think before answering

1. This is a position question and it is easy to answer as a binary. The signal is whether you separate what fits technically from what is worth paying for on every request.
2. Concede the valid half first: bigger windows genuinely absorb part of the use case. For an internal tool over a few dozen stable documents with low traffic, stuffing everything in is the right call and building a retrieval stack would be over-engineering.
3. Then give three reasons it does not absorb the rest. Cost is the first: context is billed per request, so the same corpus is paid for on every one of ten thousand queries, whereas retrieval only pays for the passages it returns. Prompt caching softens this but does not remove it.
4. Scale is the second: enterprise corpora run to hundreds of thousands of documents and no window holds them. Attribution and access control are the third: pointing an answer at a specific passage, and showing each user only what they are permitted to see, both have to happen before the material reaches the model.
5. Add the empirical point: as the supplied material grows, models become less reliable at locating the one relevant fact inside it. More context is not automatically better; fewer and more precise passages often win.
6. Expected follow-up: does retrieval change shape? Yes. Larger windows allow bigger chunks and more of them, which relieves pressure on reranking and compression. Retrieval gets coarser, it does not disappear.

分析过程 · 先想清楚再作答

1. 这是一道立场题，容易答成非黑即白。判断你有没有做过的地方在于：会不会区分「技术上能不能塞进去」和「工程上该不该每次都塞」，只谈前者的答案一听就是纸上谈兵。
2. 先承认对方有道理的部分：窗口变大确实吃掉了检索的一部分场景。几十篇文档、更新不频繁、调用量不大的内部工具，直接全塞是最省事的选择，为它建一套检索系统是过度设计。
3. 再给三条它吃不掉的理由。第一是成本：材料是按次计费的，同一份材料被问一万次就要付一万次，而检索只付取回的那几段；预填充缓存能缓解但不能消除，缓存也有有效期和命中率。
4. 第二是规模：企业知识库动辄几十万篇，再大的窗口也塞不下，检索是唯一的入口。第三是归因与权限：答案要指回具体某一段，以及不同的人只能看到自己有权访问的材料——这两件事必须在把材料喂给模型之前完成，窗口再大也不解决。
5. 还要补一条经验事实：材料变多之后，模型在长上下文里定位关键信息的稳定性会下降，出现「读了但没读到」。所以「全塞」并不总是等于「效果更好」，很多时候少而准反而更好。
6. 可预期的追问：那检索的形态会不会变？会——窗口变大之后，取回的块可以更大、条数可以更多，重排与压缩的压力变小，检索从「精挑几句」变成「粗筛一批」。趋势是检索的粒度变粗，不是检索消失。